Occlusion Handling in Radar for Detection of Obstacles Based on Tracking Model

Segal, Shai; Логвиненко, А. С.; Slapak, Alon

doi:10.23919/irs.2018.8448235

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…The works in [15] [16] covers the topic in great detail for the interested reader. Some of the recent works are mentioned in [17] [18] [19]. Hou [20] presented a similar method of fusing range information from radar data with orientation information (azimuth, elevation) from image for target tracking.…”

Section: Radar Tracking and Data Fusionmentioning

confidence: 99%

Target Tracking in Blind Range of Radars With Deep Learning

Chandrakanth

Murthy

Channappayya

2020

2020 21st International Radar Symposium (IRS)

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Surveillance radars form the first line of defense in border areas. But due to highly uneven terrains, there are pockets of vulnerability for the enemy to move undetected till they are in the blind range of the radar. This class of targets are termed the 'pop up' targets. They pose a serious threat as they can inflict severe damage to life and property. Blind ranges occur by way of design in pulsed radars. To minimize the blind range problem, multistatic radar configuration or dual pulse transmission methods were proposed. Multistatic radar configuration is highly hardware intensive and dual pulse transmission could only reduce the blind range, not eliminate it. In this work we propose, elimination of blind range using deep learning based video tracking for mono static surveillance radars. Since radars operate in deploy and forget mode, visual system must also operate in a similar way for added advantage. Deep Learning paved way for automatic target detection and classification. However, a deep learning architecture is inherently not capable of tracking because of frame to frame independence in processing. To overcome this limitation, we use prior information from past detections to establish frame to frame correlation and predict future positions of target using a method inspired from CFAR in a parallel channel for target tracking.

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Section: Radar Tracking and Data Fusionmentioning

confidence: 99%

Target Tracking in Blind Range of Radars With Deep Learning

Chandrakanth

Murthy

Channappayya

2020

2020 21st International Radar Symposium (IRS)

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“…Object existence FN/FP ⋆[93], ∇[87], ♣[94] Object separation error ⋆[95], ∇[87], ♣[4] Object state error ⋆[95], ∇[87], ♣[96] Object classification error ⋆[95], ∇[87], ♣[94] Latency ⋆[97], ∇[98] , ♣[97] …”

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confidence: 99%

Towards Serious Perception Sensor Simulation for Safety Validation of Automated Driving - A Collaborative Method to Specify Sensor Models

Linnhoff

Rosenberger

Schmidt

et al. 2021

2021 IEEE International Intelligent Transportation Systems Conference (ITSC)

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Perception sensor modeling is essential for the safety validation of automated driving systems in virtual environments. Nevertheless, the community lacks a methodical approach to derive requirements for such sensor models that enables a serious application for safety validation in the first place. This article provides a method to specify sensor models for the environmental perception of automated driving systems.The key of the approach is a collaborative collection of causeeffect chains as the basis for specification. With this collection at hand, a tabular form is introduced to extract the relevance of the effect chains to be modeled. Combined profound expert assessments in the table enable the test engineer to specify sensor models within a traceable decision-making process.This work received funding from SET Level and VVM of the PEGASUS project family, promoted by the German Federal Ministry for Economic Affairs and Energy based on a decision of the Deutsche Bundestag.

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Occlusion Handling in Radar for Detection of Obstacles Based on Tracking Model

Cited by 2 publications

References 11 publications

Target Tracking in Blind Range of Radars With Deep Learning

Target Tracking in Blind Range of Radars With Deep Learning

Towards Serious Perception Sensor Simulation for Safety Validation of Automated Driving - A Collaborative Method to Specify Sensor Models

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